Overview

Dataset statistics

Number of variables27
Number of observations575
Missing cells89
Missing cells (%)0.6%
Duplicate rows20
Duplicate rows (%)3.5%
Total size in memory121.4 KiB
Average record size in memory216.2 B

Variable types

Categorical11
Numeric12
Text4

Alerts

artist_id has constant value ""Constant
artist_name has constant value ""Constant
artist_popularity has constant value ""Constant
Dataset has 20 (3.5%) duplicate rowsDuplicates
disc_number is highly imbalanced (80.8%)Imbalance
explicit is highly imbalanced (73.1%)Imbalance
audio_features.mode is highly imbalanced (56.8%)Imbalance
audio_features.time_signature is highly imbalanced (83.6%)Imbalance
track_id has 8 (1.4%) missing valuesMissing
track_name has 7 (1.2%) missing valuesMissing
album_name has 62 (10.8%) missing valuesMissing
audio_features.key has 111 (19.3%) zerosZeros

Reproduction

Analysis started2024-01-08 01:36:02.022781
Analysis finished2024-01-08 01:36:37.576972
Duration35.55 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

disc_number
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
1
558 
2
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters575
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 558
97.0%
2 17
 
3.0%

Length

2024-01-07T20:36:37.765324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-07T20:36:38.006032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 558
97.0%
2 17
 
3.0%

Most occurring characters

ValueCountFrequency (%)
1 558
97.0%
2 17
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 575
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 558
97.0%
2 17
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 575
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 558
97.0%
2 17
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 575
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 558
97.0%
2 17
 
3.0%

duration_ms
Real number (ℝ)

Distinct364
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234136.99
Minimum-223093
Maximum613026
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.3%
Memory size4.6 KiB
2024-01-07T20:36:38.278372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-223093
5-th percentile173290
Q1207440
median231706
Q3256741.5
95-th percentile313018.7
Maximum613026
Range836119
Interquartile range (IQR)49301.5

Descriptive statistics

Standard deviation54527.343
Coefficient of variation (CV)0.23288649
Kurtosis16.279153
Mean234136.99
Median Absolute Deviation (MAD)24418
Skewness-0.7768524
Sum1.3462877 × 108
Variance2.9732311 × 109
MonotonicityNot monotonic
2024-01-07T20:36:38.605141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231000 6
 
1.0%
212600 5
 
0.9%
235800 5
 
0.9%
247533 5
 
0.9%
190240 4
 
0.7%
150440 4
 
0.7%
193000 4
 
0.7%
223293 4
 
0.7%
171360 4
 
0.7%
200306 4
 
0.7%
Other values (354) 530
92.2%
ValueCountFrequency (%)
-223093 1
 
0.2%
-107133 1
 
0.2%
10 1
 
0.2%
1000 1
 
0.2%
3000 1
 
0.2%
83253 1
 
0.2%
131186 1
 
0.2%
146436 2
0.3%
148781 2
0.3%
150440 4
0.7%
ValueCountFrequency (%)
613026 1
0.2%
405906 1
0.2%
404680 1
0.2%
403933 1
0.2%
403887 1
0.2%
389213 1
0.2%
376466 1
0.2%
369120 1
0.2%
369066 1
0.2%
367146 2
0.3%

explicit
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
False
515 
True
54 
No
 
5
Si
 
1

Length

Max length5
Median length5
Mean length4.8747826
Min length2

Characters and Unicode

Total characters2803
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowFalse
2nd rowFalse
3rd rowFalse
4th rowFalse
5th rowFalse

Common Values

ValueCountFrequency (%)
False 515
89.6%
True 54
 
9.4%
No 5
 
0.9%
Si 1
 
0.2%

Length

2024-01-07T20:36:38.916415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-07T20:36:39.156918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
false 515
89.6%
true 54
 
9.4%
no 5
 
0.9%
si 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 569
20.3%
F 515
18.4%
a 515
18.4%
l 515
18.4%
s 515
18.4%
T 54
 
1.9%
r 54
 
1.9%
u 54
 
1.9%
N 5
 
0.2%
o 5
 
0.2%
Other values (2) 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2228
79.5%
Uppercase Letter 575
 
20.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 569
25.5%
a 515
23.1%
l 515
23.1%
s 515
23.1%
r 54
 
2.4%
u 54
 
2.4%
o 5
 
0.2%
i 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
F 515
89.6%
T 54
 
9.4%
N 5
 
0.9%
S 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 2803
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 569
20.3%
F 515
18.4%
a 515
18.4%
l 515
18.4%
s 515
18.4%
T 54
 
1.9%
r 54
 
1.9%
u 54
 
1.9%
N 5
 
0.2%
o 5
 
0.2%
Other values (2) 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 569
20.3%
F 515
18.4%
a 515
18.4%
l 515
18.4%
s 515
18.4%
T 54
 
1.9%
r 54
 
1.9%
u 54
 
1.9%
N 5
 
0.2%
o 5
 
0.2%
Other values (2) 2
 
0.1%

track_number
Real number (ℝ)

Distinct46
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.168696
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-01-07T20:36:39.605514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile25
Maximum46
Range45
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.8323724
Coefficient of variation (CV)0.70127906
Kurtosis2.9862529
Mean11.168696
Median Absolute Deviation (MAD)5
Skewness1.3713022
Sum6422
Variance61.346057
MonotonicityNot monotonic
2024-01-07T20:36:39.914867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
8 31
 
5.4%
1 30
 
5.2%
6 30
 
5.2%
7 30
 
5.2%
2 30
 
5.2%
5 30
 
5.2%
3 30
 
5.2%
4 30
 
5.2%
11 29
 
5.0%
12 29
 
5.0%
Other values (36) 276
48.0%
ValueCountFrequency (%)
1 30
5.2%
2 30
5.2%
3 30
5.2%
4 30
5.2%
5 30
5.2%
6 30
5.2%
7 30
5.2%
8 31
5.4%
9 29
5.0%
10 29
5.0%
ValueCountFrequency (%)
46 1
0.2%
45 1
0.2%
44 1
0.2%
43 1
0.2%
42 1
0.2%
41 1
0.2%
40 1
0.2%
39 1
0.2%
38 1
0.2%
37 1
0.2%

track_popularity
Real number (ℝ)

Distinct73
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.113043
Minimum-92
Maximum152
Zeros3
Zeros (%)0.5%
Negative6
Negative (%)1.0%
Memory size4.6 KiB
2024-01-07T20:36:40.261636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-92
5-th percentile34
Q152.5
median70
Q378
95-th percentile86
Maximum152
Range244
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation22.323237
Coefficient of variation (CV)0.34818558
Kurtosis15.572581
Mean64.113043
Median Absolute Deviation (MAD)11
Skewness-2.7643922
Sum36865
Variance498.32692
MonotonicityNot monotonic
2024-01-07T20:36:40.669362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 24
 
4.2%
70 24
 
4.2%
82 22
 
3.8%
80 22
 
3.8%
78 21
 
3.7%
77 17
 
3.0%
76 17
 
3.0%
68 16
 
2.8%
71 16
 
2.8%
74 15
 
2.6%
Other values (63) 381
66.3%
ValueCountFrequency (%)
-92 1
 
0.2%
-85 1
 
0.2%
-75 1
 
0.2%
-71 1
 
0.2%
-70 1
 
0.2%
-69 1
 
0.2%
0 3
0.5%
30 1
 
0.2%
31 1
 
0.2%
32 3
0.5%
ValueCountFrequency (%)
152 1
 
0.2%
99 4
0.7%
94 1
 
0.2%
92 4
0.7%
91 3
 
0.5%
90 2
 
0.3%
89 1
 
0.2%
88 4
0.7%
87 6
1.0%
86 9
1.6%

track_id
Text

MISSING 

Distinct512
Distinct (%)90.3%
Missing8
Missing (%)1.4%
Memory size4.6 KiB
2024-01-07T20:36:41.048026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters12474
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique493 ?
Unique (%)86.9%

Sample

1st row4WUepByoeqcedHoYhSNHRt
2nd row0108kcWLnn2HlH2kedi1gn
3rd row3Vpk1hfMAQme8VJ0SNRSkd
4th row1OcSfkeCg9hRC2sFKB4IMJ
5th row2k0ZEeAqzvYMcx9Qt5aClQ
ValueCountFrequency (%)
43ra71bccxfgd4c8gopiln 4
 
0.7%
5hqsxkfgbxjzo9ucwd11so 4
 
0.7%
1dgr1c8crmldpv6mpbimsi 4
 
0.7%
3phkh7d0lzm2aldutz2x37 4
 
0.7%
1fzauuvbzlhz1ljax9pty6 4
 
0.7%
1smiq65isabpto6gpflbym 4
 
0.7%
2rk4jlnc2tpmze2af99d45 4
 
0.7%
1symezit3h8uzfibcs3tyi 4
 
0.7%
6rrnnciqgzexnqk8sq9yv5 4
 
0.7%
1bxfupkguatgp7am0bbdwr 4
 
0.7%
Other values (502) 527
92.9%
2024-01-07T20:36:41.642910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 288
 
2.3%
2 279
 
2.2%
1 271
 
2.2%
0 256
 
2.1%
5 256
 
2.1%
3 247
 
2.0%
7 246
 
2.0%
6 232
 
1.9%
9 224
 
1.8%
c 220
 
1.8%
Other values (52) 9955
79.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5092
40.8%
Uppercase Letter 4908
39.3%
Decimal Number 2474
19.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 220
 
4.3%
k 213
 
4.2%
y 213
 
4.2%
m 209
 
4.1%
q 208
 
4.1%
x 207
 
4.1%
d 207
 
4.1%
w 207
 
4.1%
g 205
 
4.0%
z 205
 
4.0%
Other values (16) 2998
58.9%
Uppercase Letter
ValueCountFrequency (%)
R 207
 
4.2%
P 207
 
4.2%
I 204
 
4.2%
Y 203
 
4.1%
U 202
 
4.1%
V 200
 
4.1%
A 199
 
4.1%
Q 198
 
4.0%
F 198
 
4.0%
Z 197
 
4.0%
Other values (16) 2893
58.9%
Decimal Number
ValueCountFrequency (%)
4 288
11.6%
2 279
11.3%
1 271
11.0%
0 256
10.3%
5 256
10.3%
3 247
10.0%
7 246
9.9%
6 232
9.4%
9 224
9.1%
8 175
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
80.2%
Common 2474
 
19.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 220
 
2.2%
k 213
 
2.1%
y 213
 
2.1%
m 209
 
2.1%
q 208
 
2.1%
x 207
 
2.1%
d 207
 
2.1%
w 207
 
2.1%
R 207
 
2.1%
P 207
 
2.1%
Other values (42) 7902
79.0%
Common
ValueCountFrequency (%)
4 288
11.6%
2 279
11.3%
1 271
11.0%
0 256
10.3%
5 256
10.3%
3 247
10.0%
7 246
9.9%
6 232
9.4%
9 224
9.1%
8 175
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 288
 
2.3%
2 279
 
2.2%
1 271
 
2.2%
0 256
 
2.1%
5 256
 
2.1%
3 247
 
2.0%
7 246
 
2.0%
6 232
 
1.9%
9 224
 
1.8%
c 220
 
1.8%
Other values (52) 9955
79.8%

track_name
Text

MISSING 

Distinct331
Distinct (%)58.3%
Missing7
Missing (%)1.2%
Memory size4.6 KiB
2024-01-07T20:36:42.218264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length84
Median length57
Mean length21.809859
Min length2

Characters and Unicode

Total characters12388
Distinct characters67
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique180 ?
Unique (%)31.7%

Sample

1st rowWelcome To New York (Taylor's Version)
2nd rowBlank Space (Taylor's Version)
3rd rowStyle (Taylor's Version)
4th rowOut Of The Woods (Taylor's Version)
5th rowAll You Had To Do Was Stay (Taylor's Version)
ValueCountFrequency (%)
the 129
 
6.0%
version 127
 
5.9%
taylor's 79
 
3.7%
71
 
3.3%
you 45
 
2.1%
taylor’s 42
 
2.0%
from 39
 
1.8%
feat 34
 
1.6%
i 33
 
1.5%
vault 31
 
1.4%
Other values (413) 1521
70.7%
2024-01-07T20:36:43.154100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1583
 
12.8%
e 1130
 
9.1%
o 858
 
6.9%
r 726
 
5.9%
a 689
 
5.6%
s 607
 
4.9%
n 592
 
4.8%
i 563
 
4.5%
t 516
 
4.2%
l 465
 
3.8%
Other values (57) 4659
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8211
66.3%
Uppercase Letter 1761
 
14.2%
Space Separator 1583
 
12.8%
Other Punctuation 225
 
1.8%
Close Punctuation 191
 
1.5%
Open Punctuation 191
 
1.5%
Decimal Number 104
 
0.8%
Dash Punctuation 63
 
0.5%
Final Punctuation 57
 
0.5%
Initial Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1130
13.8%
o 858
10.4%
r 726
8.8%
a 689
 
8.4%
s 607
 
7.4%
n 592
 
7.2%
i 563
 
6.9%
t 516
 
6.3%
l 465
 
5.7%
h 331
 
4.0%
Other values (15) 1734
21.1%
Uppercase Letter
ValueCountFrequency (%)
T 314
17.8%
V 164
 
9.3%
S 132
 
7.5%
B 97
 
5.5%
W 87
 
4.9%
F 84
 
4.8%
L 81
 
4.6%
I 80
 
4.5%
M 76
 
4.3%
O 69
 
3.9%
Other values (14) 577
32.8%
Other Punctuation
ValueCountFrequency (%)
' 104
46.2%
. 64
28.4%
/ 18
 
8.0%
& 10
 
4.4%
! 10
 
4.4%
, 9
 
4.0%
? 6
 
2.7%
" 4
 
1.8%
Decimal Number
ValueCountFrequency (%)
1 35
33.7%
0 32
30.8%
2 29
27.9%
8 8
 
7.7%
Space Separator
ValueCountFrequency (%)
1583
100.0%
Close Punctuation
ValueCountFrequency (%)
) 191
100.0%
Open Punctuation
ValueCountFrequency (%)
( 191
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 57
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9972
80.5%
Common 2416
 
19.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1130
 
11.3%
o 858
 
8.6%
r 726
 
7.3%
a 689
 
6.9%
s 607
 
6.1%
n 592
 
5.9%
i 563
 
5.6%
t 516
 
5.2%
l 465
 
4.7%
h 331
 
3.3%
Other values (39) 3495
35.0%
Common
ValueCountFrequency (%)
1583
65.5%
) 191
 
7.9%
( 191
 
7.9%
' 104
 
4.3%
. 64
 
2.6%
- 63
 
2.6%
’ 57
 
2.4%
1 35
 
1.4%
0 32
 
1.3%
2 29
 
1.2%
Other values (8) 67
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12329
99.5%
Punctuation 59
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1583
 
12.8%
e 1130
 
9.2%
o 858
 
7.0%
r 726
 
5.9%
a 689
 
5.6%
s 607
 
4.9%
n 592
 
4.8%
i 563
 
4.6%
t 516
 
4.2%
l 465
 
3.8%
Other values (55) 4600
37.3%
Punctuation
ValueCountFrequency (%)
’ 57
96.6%
‘ 2
 
3.4%
Distinct267
Distinct (%)46.6%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean0.59170157
Minimum0.243
Maximum0.897
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-01-07T20:36:43.501427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.243
5-th percentile0.3614
Q10.519
median0.599
Q30.664
95-th percentile0.7838
Maximum0.897
Range0.654
Interquartile range (IQR)0.145

Descriptive statistics

Standard deviation0.1212435
Coefficient of variation (CV)0.2049065
Kurtosis-0.043867526
Mean0.59170157
Median Absolute Deviation (MAD)0.073
Skewness-0.20934246
Sum339.045
Variance0.014699986
MonotonicityNot monotonic
2024-01-07T20:36:43.826983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.61 9
 
1.6%
0.602 9
 
1.6%
0.359 7
 
1.2%
0.546 6
 
1.0%
0.535 6
 
1.0%
0.553 6
 
1.0%
0.664 6
 
1.0%
0.649 6
 
1.0%
0.575 6
 
1.0%
0.662 6
 
1.0%
Other values (257) 506
88.0%
ValueCountFrequency (%)
0.243 1
 
0.2%
0.292 4
0.7%
0.298 1
 
0.2%
0.31 2
0.3%
0.313 2
0.3%
0.316 2
0.3%
0.317 2
0.3%
0.327 1
 
0.2%
0.334 1
 
0.2%
0.335 1
 
0.2%
ValueCountFrequency (%)
0.897 4
0.7%
0.87 1
 
0.2%
0.867 1
 
0.2%
0.843 3
0.5%
0.828 1
 
0.2%
0.824 4
0.7%
0.815 1
 
0.2%
0.811 4
0.7%
0.81 1
 
0.2%
0.8 3
0.5%

audio_features.energy
Real number (ℝ)

Distinct348
Distinct (%)60.7%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean0.57131588
Minimum0.118
Maximum0.949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-01-07T20:36:44.153039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.118
5-th percentile0.238
Q10.435
median0.589
Q30.725
95-th percentile0.8532
Maximum0.949
Range0.831
Interquartile range (IQR)0.29

Descriptive statistics

Standard deviation0.19265068
Coefficient of variation (CV)0.33720518
Kurtosis-0.77312531
Mean0.57131588
Median Absolute Deviation (MAD)0.143
Skewness-0.2606033
Sum327.364
Variance0.037114283
MonotonicityNot monotonic
2024-01-07T20:36:44.469623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.747 7
 
1.2%
0.574 6
 
1.0%
0.634 6
 
1.0%
0.488 6
 
1.0%
0.366 5
 
0.9%
0.543 5
 
0.9%
0.777 5
 
0.9%
0.732 5
 
0.9%
0.624 5
 
0.9%
0.658 5
 
0.9%
Other values (338) 518
90.1%
ValueCountFrequency (%)
0.118 1
0.2%
0.128 1
0.2%
0.131 1
0.2%
0.151 1
0.2%
0.155 1
0.2%
0.156 1
0.2%
0.16 1
0.2%
0.161 1
0.2%
0.166 1
0.2%
0.172 1
0.2%
ValueCountFrequency (%)
0.949 1
0.2%
0.944 2
0.3%
0.934 1
0.2%
0.933 1
0.2%
0.917 2
0.3%
0.915 2
0.3%
0.909 1
0.2%
0.908 1
0.2%
0.902 1
0.2%
0.899 1
0.2%

audio_features.key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)2.1%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean4.5714286
Minimum0
Maximum11
Zeros111
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-01-07T20:36:44.737700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3275442
Coefficient of variation (CV)0.7279003
Kurtosis-1.1327608
Mean4.5714286
Median Absolute Deviation (MAD)3
Skewness0.089003933
Sum2624
Variance11.07255
MonotonicityNot monotonic
2024-01-07T20:36:44.985626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 111
19.3%
7 97
16.9%
5 68
11.8%
4 64
11.1%
2 60
10.4%
9 44
 
7.7%
1 28
 
4.9%
10 24
 
4.2%
6 24
 
4.2%
8 23
 
4.0%
Other values (2) 31
 
5.4%
ValueCountFrequency (%)
0 111
19.3%
1 28
 
4.9%
2 60
10.4%
3 13
 
2.3%
4 64
11.1%
5 68
11.8%
6 24
 
4.2%
7 97
16.9%
8 23
 
4.0%
9 44
 
7.7%
ValueCountFrequency (%)
11 18
 
3.1%
10 24
 
4.2%
9 44
7.7%
8 23
 
4.0%
7 97
16.9%
6 24
 
4.2%
5 68
11.8%
4 64
11.1%
3 13
 
2.3%
2 60
10.4%

audio_features.loudness
Real number (ℝ)

Distinct448
Distinct (%)78.2%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean-7.5515899
Minimum-17.932
Maximum-1.909
Zeros0
Zeros (%)0.0%
Negative573
Negative (%)99.7%
Memory size4.6 KiB
2024-01-07T20:36:45.270898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-17.932
5-th percentile-12.879
Q1-9.375
median-7.031
Q3-5.398
95-th percentile-3.6296
Maximum-1.909
Range16.023
Interquartile range (IQR)3.977

Descriptive statistics

Standard deviation2.8949108
Coefficient of variation (CV)-0.38335117
Kurtosis0.036394484
Mean-7.5515899
Median Absolute Deviation (MAD)1.981
Skewness-0.60249272
Sum-4327.061
Variance8.3805084
MonotonicityNot monotonic
2024-01-07T20:36:45.620750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.754 4
 
0.7%
-6.639 4
 
0.7%
-10.813 4
 
0.7%
-9.602 4
 
0.7%
-9.912 4
 
0.7%
-4.105 4
 
0.7%
-8.746 4
 
0.7%
-5.617 4
 
0.7%
-10.862 4
 
0.7%
-12.566 4
 
0.7%
Other values (438) 533
92.7%
ValueCountFrequency (%)
-17.932 1
0.2%
-16.394 1
0.2%
-15.91 1
0.2%
-15.489 1
0.2%
-15.48 1
0.2%
-15.434 1
0.2%
-15.065 1
0.2%
-15.064 1
0.2%
-15.01 2
0.3%
-14.958 1
0.2%
ValueCountFrequency (%)
-1.909 1
0.2%
-1.953 1
0.2%
-2.098 1
0.2%
-2.347 1
0.2%
-2.608 1
0.2%
-2.622 1
0.2%
-2.641 2
0.3%
-2.846 1
0.2%
-2.871 1
0.2%
-2.881 1
0.2%

audio_features.mode
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
1
524 
0
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters575
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 524
91.1%
0 51
 
8.9%

Length

2024-01-07T20:36:45.940804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-07T20:36:46.148293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 524
91.1%
0 51
 
8.9%

Most occurring characters

ValueCountFrequency (%)
1 524
91.1%
0 51
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 575
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 524
91.1%
0 51
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common 575
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 524
91.1%
0 51
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 575
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 524
91.1%
0 51
 
8.9%
Distinct292
Distinct (%)50.9%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.060306446
Minimum0.0231
Maximum0.912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-01-07T20:36:46.415218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0231
5-th percentile0.0264
Q10.030825
median0.03905
Q30.05725
95-th percentile0.16435
Maximum0.912
Range0.8889
Interquartile range (IQR)0.026425

Descriptive statistics

Standard deviation0.076884373
Coefficient of variation (CV)1.2748948
Kurtosis48.021452
Mean0.060306446
Median Absolute Deviation (MAD)0.01085
Skewness6.1434797
Sum34.6159
Variance0.0059112067
MonotonicityNot monotonic
2024-01-07T20:36:46.742713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0401 8
 
1.4%
0.0275 7
 
1.2%
0.0308 6
 
1.0%
0.0264 6
 
1.0%
0.054 5
 
0.9%
0.0344 5
 
0.9%
0.0363 5
 
0.9%
0.05 5
 
0.9%
0.0641 5
 
0.9%
0.0323 5
 
0.9%
Other values (282) 517
89.9%
ValueCountFrequency (%)
0.0231 1
 
0.2%
0.0234 1
 
0.2%
0.0239 1
 
0.2%
0.0243 3
0.5%
0.0244 1
 
0.2%
0.0245 1
 
0.2%
0.0246 1
 
0.2%
0.025 1
 
0.2%
0.0251 1
 
0.2%
0.0252 1
 
0.2%
ValueCountFrequency (%)
0.912 1
 
0.2%
0.721 1
 
0.2%
0.589 1
 
0.2%
0.519 4
0.7%
0.39 1
 
0.2%
0.364 1
 
0.2%
0.363 1
 
0.2%
0.245 2
0.3%
0.239 4
0.7%
0.201 1
 
0.2%
Distinct401
Distinct (%)69.9%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.33755142
Minimum-0.00354
Maximum5
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.3%
Memory size4.6 KiB
2024-01-07T20:36:47.053993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.00354
5-th percentile0.0025085
Q10.03485
median0.163
Q30.664
95-th percentile0.907
Maximum5
Range5.00354
Interquartile range (IQR)0.62915

Descriptive statistics

Standard deviation0.39238262
Coefficient of variation (CV)1.1624381
Kurtosis33.710657
Mean0.33755142
Median Absolute Deviation (MAD)0.154775
Skewness3.4269372
Sum193.75451
Variance0.15396412
MonotonicityNot monotonic
2024-01-07T20:36:47.383771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 7
 
1.2%
0.298 5
 
0.9%
0.101 5
 
0.9%
0.0129 5
 
0.9%
0.454 4
 
0.7%
0.117 4
 
0.7%
0.492 4
 
0.7%
0.0767 4
 
0.7%
0.00889 4
 
0.7%
0.028 4
 
0.7%
Other values (391) 528
91.8%
ValueCountFrequency (%)
-0.00354 1
0.2%
-0.000537 1
0.2%
0.000184 1
0.2%
0.000191 1
0.2%
0.000197 1
0.2%
0.000315 2
0.3%
0.000328 1
0.2%
0.000418 1
0.2%
0.000421 1
0.2%
0.000443 1
0.2%
ValueCountFrequency (%)
5 1
 
0.2%
2 1
 
0.2%
1.5 1
 
0.2%
0.971 4
0.7%
0.967 2
0.3%
0.966 1
 
0.2%
0.964 3
0.5%
0.962 1
 
0.2%
0.946 1
 
0.2%
0.942 1
 
0.2%
Distinct240
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-01-07T20:36:47.799498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.7947826
Min length1

Characters and Unicode

Total characters2757
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179 ?
Unique (%)31.1%

Sample

1st row3.66e-05
2nd row0
3rd row0.0197
4th row5.59e-05
5th row0
ValueCountFrequency (%)
0 247
43.0%
2.06e-05 6
 
1.0%
2.03e-06 4
 
0.7%
1.58e-05 4
 
0.7%
0.00569 4
 
0.7%
0.000353 4
 
0.7%
0.00615 4
 
0.7%
1.36e-05 4
 
0.7%
0.000189 4
 
0.7%
0.000104 4
 
0.7%
Other values (230) 290
50.4%
2024-01-07T20:36:48.545095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 924
33.5%
. 327
 
11.9%
- 200
 
7.3%
e 199
 
7.2%
5 184
 
6.7%
6 180
 
6.5%
1 179
 
6.5%
3 117
 
4.2%
2 116
 
4.2%
7 96
 
3.5%
Other values (4) 235
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2030
73.6%
Other Punctuation 327
 
11.9%
Dash Punctuation 200
 
7.3%
Lowercase Letter 200
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 924
45.5%
5 184
 
9.1%
6 180
 
8.9%
1 179
 
8.8%
3 117
 
5.8%
2 116
 
5.7%
7 96
 
4.7%
4 95
 
4.7%
8 71
 
3.5%
9 68
 
3.3%
Lowercase Letter
ValueCountFrequency (%)
e 199
99.5%
x 1
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 327
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2557
92.7%
Latin 200
 
7.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 924
36.1%
. 327
 
12.8%
- 200
 
7.8%
5 184
 
7.2%
6 180
 
7.0%
1 179
 
7.0%
3 117
 
4.6%
2 116
 
4.5%
7 96
 
3.8%
4 95
 
3.7%
Other values (2) 139
 
5.4%
Latin
ValueCountFrequency (%)
e 199
99.5%
x 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2757
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 924
33.5%
. 327
 
11.9%
- 200
 
7.3%
e 199
 
7.2%
5 184
 
6.7%
6 180
 
6.5%
1 179
 
6.5%
3 117
 
4.2%
2 116
 
4.2%
7 96
 
3.5%
Other values (4) 235
 
8.5%

audio_features.liveness
Real number (ℝ)

Distinct271
Distinct (%)47.2%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.16029268
Minimum0.0335
Maximum0.931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-01-07T20:36:48.900494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0335
5-th percentile0.0663
Q10.0948
median0.115
Q30.158
95-th percentile0.36805
Maximum0.931
Range0.8975
Interquartile range (IQR)0.0632

Descriptive statistics

Standard deviation0.13849843
Coefficient of variation (CV)0.86403464
Kurtosis12.638741
Mean0.16029268
Median Absolute Deviation (MAD)0.025
Skewness3.336118
Sum92.008
Variance0.019181815
MonotonicityNot monotonic
2024-01-07T20:36:49.220318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 14
 
2.4%
0.108 13
 
2.3%
0.118 12
 
2.1%
0.101 11
 
1.9%
0.106 11
 
1.9%
0.114 10
 
1.7%
0.123 10
 
1.7%
0.105 10
 
1.7%
0.115 10
 
1.7%
0.121 8
 
1.4%
Other values (261) 465
80.9%
ValueCountFrequency (%)
0.0335 1
0.2%
0.0357 1
0.2%
0.0391 1
0.2%
0.0419 2
0.3%
0.0437 1
0.2%
0.0473 1
0.2%
0.0477 1
0.2%
0.054 1
0.2%
0.0574 1
0.2%
0.0576 1
0.2%
ValueCountFrequency (%)
0.931 1
0.2%
0.918 1
0.2%
0.889 1
0.2%
0.884 1
0.2%
0.867 1
0.2%
0.865 1
0.2%
0.837 1
0.2%
0.83 1
0.2%
0.815 1
0.2%
0.795 1
0.2%

audio_features.valence
Real number (ℝ)

Distinct326
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40360852
Minimum0.0374
Maximum0.943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-01-07T20:36:49.604865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0374
5-th percentile0.1087
Q10.2385
median0.397
Q30.539
95-th percentile0.7472
Maximum0.943
Range0.9056
Interquartile range (IQR)0.3005

Descriptive statistics

Standard deviation0.19912425
Coefficient of variation (CV)0.49335988
Kurtosis-0.45643106
Mean0.40360852
Median Absolute Deviation (MAD)0.148
Skewness0.3576049
Sum232.0749
Variance0.039650468
MonotonicityNot monotonic
2024-01-07T20:36:49.954878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.545 9
 
1.6%
0.399 7
 
1.2%
0.541 6
 
1.0%
0.351 6
 
1.0%
0.714 6
 
1.0%
0.633 5
 
0.9%
0.166 5
 
0.9%
0.472 5
 
0.9%
0.328 5
 
0.9%
0.248 5
 
0.9%
Other values (316) 516
89.7%
ValueCountFrequency (%)
0.0374 1
0.2%
0.0382 2
0.3%
0.0499 1
0.2%
0.0567 1
0.2%
0.0586 1
0.2%
0.0633 1
0.2%
0.0662 1
0.2%
0.068 1
0.2%
0.0682 1
0.2%
0.0734 1
0.2%
ValueCountFrequency (%)
0.943 1
 
0.2%
0.942 1
 
0.2%
0.928 1
 
0.2%
0.921 1
 
0.2%
0.92 2
0.3%
0.917 1
 
0.2%
0.865 4
0.7%
0.84 2
0.3%
0.838 1
 
0.2%
0.826 1
 
0.2%

audio_features.tempo
Real number (ℝ)

Distinct450
Distinct (%)78.4%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean122.21275
Minimum68.097
Maximum208.918
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-01-07T20:36:50.440841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum68.097
5-th percentile79.015
Q196.0685
median118.962
Q3143.95
95-th percentile176.28255
Maximum208.918
Range140.821
Interquartile range (IQR)47.8815

Descriptive statistics

Standard deviation31.119316
Coefficient of variation (CV)0.25463232
Kurtosis-0.34852932
Mean122.21275
Median Absolute Deviation (MAD)22.967
Skewness0.50084188
Sum70150.118
Variance968.41182
MonotonicityNot monotonic
2024-01-07T20:36:50.760849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.008 4
 
0.7%
102.012 4
 
0.7%
149.983 4
 
0.7%
92.875 4
 
0.7%
169.994 4
 
0.7%
68.534 4
 
0.7%
124.344 4
 
0.7%
100.003 4
 
0.7%
150.088 4
 
0.7%
103.979 4
 
0.7%
Other values (440) 534
92.9%
ValueCountFrequency (%)
68.097 1
 
0.2%
68.534 4
0.7%
70.008 4
0.7%
71.981 1
 
0.2%
73.849 1
 
0.2%
73.942 1
 
0.2%
74.9 1
 
0.2%
74.952 2
0.3%
74.957 2
0.3%
75.602 1
 
0.2%
ValueCountFrequency (%)
208.918 1
 
0.2%
207.476 4
0.7%
204.489 1
 
0.2%
203.959 2
0.3%
203.89 1
 
0.2%
202.319 1
 
0.2%
200.391 1
 
0.2%
200.017 1
 
0.2%
199.997 1
 
0.2%
185.972 1
 
0.2%
Distinct519
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2024-01-07T20:36:51.121713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters12650
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique499 ?
Unique (%)86.8%

Sample

1st row4WUepByoeqcedHoYhSNHRt
2nd row0108kcWLnn2HlH2kedi1gn
3rd row3Vpk1hfMAQme8VJ0SNRSkd
4th row1OcSfkeCg9hRC2sFKB4IMJ
5th row2k0ZEeAqzvYMcx9Qt5aClQ
ValueCountFrequency (%)
4y5bvroubdpr5fuwxbibzr 4
 
0.7%
3phkh7d0lzm2aldutz2x37 4
 
0.7%
1symezit3h8uzfibcs3tyi 4
 
0.7%
1fzauuvbzlhz1ljax9pty6 4
 
0.7%
1smiq65isabpto6gpflbym 4
 
0.7%
43ra71bccxfgd4c8gopiln 4
 
0.7%
1bxfupkguatgp7am0bbdwr 4
 
0.7%
1dgr1c8crmldpv6mpbimsi 4
 
0.7%
3rauevgrgj1iuwdj9fds70 4
 
0.7%
6rrnnciqgzexnqk8sq9yv5 4
 
0.7%
Other values (509) 535
93.0%
2024-01-07T20:36:51.730959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 291
 
2.3%
2 284
 
2.2%
1 277
 
2.2%
0 264
 
2.1%
5 260
 
2.1%
7 252
 
2.0%
3 252
 
2.0%
6 234
 
1.8%
9 225
 
1.8%
c 221
 
1.7%
Other values (52) 10090
79.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5160
40.8%
Uppercase Letter 4975
39.3%
Decimal Number 2515
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 221
 
4.3%
y 216
 
4.2%
q 214
 
4.1%
m 214
 
4.1%
k 214
 
4.1%
x 213
 
4.1%
w 210
 
4.1%
z 209
 
4.1%
d 208
 
4.0%
g 207
 
4.0%
Other values (16) 3034
58.8%
Uppercase Letter
ValueCountFrequency (%)
R 211
 
4.2%
P 210
 
4.2%
I 208
 
4.2%
Y 208
 
4.2%
A 203
 
4.1%
F 203
 
4.1%
U 202
 
4.1%
V 200
 
4.0%
Z 200
 
4.0%
Q 199
 
4.0%
Other values (16) 2931
58.9%
Decimal Number
ValueCountFrequency (%)
4 291
11.6%
2 284
11.3%
1 277
11.0%
0 264
10.5%
5 260
10.3%
7 252
10.0%
3 252
10.0%
6 234
9.3%
9 225
8.9%
8 176
7.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10135
80.1%
Common 2515
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 221
 
2.2%
y 216
 
2.1%
q 214
 
2.1%
m 214
 
2.1%
k 214
 
2.1%
x 213
 
2.1%
R 211
 
2.1%
P 210
 
2.1%
w 210
 
2.1%
z 209
 
2.1%
Other values (42) 8003
79.0%
Common
ValueCountFrequency (%)
4 291
11.6%
2 284
11.3%
1 277
11.0%
0 264
10.5%
5 260
10.3%
7 252
10.0%
3 252
10.0%
6 234
9.3%
9 225
8.9%
8 176
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 291
 
2.3%
2 284
 
2.2%
1 277
 
2.2%
0 264
 
2.1%
5 260
 
2.1%
7 252
 
2.0%
3 252
 
2.0%
6 234
 
1.8%
9 225
 
1.8%
c 221
 
1.7%
Other values (52) 10090
79.8%

audio_features.time_signature
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size4.6 KiB
4.0
553 
3.0
 
14
5.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1722
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 553
96.2%
3.0 14
 
2.4%
5.0 7
 
1.2%
(Missing) 1
 
0.2%

Length

2024-01-07T20:36:52.050760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-07T20:36:52.268227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 553
96.3%
3.0 14
 
2.4%
5.0 7
 
1.2%

Most occurring characters

ValueCountFrequency (%)
. 574
33.3%
0 574
33.3%
4 553
32.1%
3 14
 
0.8%
5 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1148
66.7%
Other Punctuation 574
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 574
50.0%
4 553
48.2%
3 14
 
1.2%
5 7
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 574
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1722
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 574
33.3%
0 574
33.3%
4 553
32.1%
3 14
 
0.8%
5 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1722
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 574
33.3%
0 574
33.3%
4 553
32.1%
3 14
 
0.8%
5 7
 
0.4%

artist_id
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
06HL4z0CvFAxyc27GX
575 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters10350
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row06HL4z0CvFAxyc27GX
2nd row06HL4z0CvFAxyc27GX
3rd row06HL4z0CvFAxyc27GX
4th row06HL4z0CvFAxyc27GX
5th row06HL4z0CvFAxyc27GX

Common Values

ValueCountFrequency (%)
06HL4z0CvFAxyc27GX 575
100.0%

Length

2024-01-07T20:36:52.504869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-07T20:36:52.709262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
06hl4z0cvfaxyc27gx 575
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1150
 
11.1%
A 575
 
5.6%
G 575
 
5.6%
7 575
 
5.6%
2 575
 
5.6%
c 575
 
5.6%
y 575
 
5.6%
x 575
 
5.6%
F 575
 
5.6%
6 575
 
5.6%
Other values (7) 4025
38.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4025
38.9%
Decimal Number 3450
33.3%
Lowercase Letter 2875
27.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 575
14.3%
G 575
14.3%
F 575
14.3%
C 575
14.3%
L 575
14.3%
H 575
14.3%
X 575
14.3%
Decimal Number
ValueCountFrequency (%)
0 1150
33.3%
7 575
16.7%
2 575
16.7%
6 575
16.7%
4 575
16.7%
Lowercase Letter
ValueCountFrequency (%)
c 575
20.0%
y 575
20.0%
x 575
20.0%
v 575
20.0%
z 575
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6900
66.7%
Common 3450
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 575
8.3%
G 575
8.3%
c 575
8.3%
y 575
8.3%
x 575
8.3%
F 575
8.3%
v 575
8.3%
C 575
8.3%
z 575
8.3%
L 575
8.3%
Other values (2) 1150
16.7%
Common
ValueCountFrequency (%)
0 1150
33.3%
7 575
16.7%
2 575
16.7%
6 575
16.7%
4 575
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1150
 
11.1%
A 575
 
5.6%
G 575
 
5.6%
7 575
 
5.6%
2 575
 
5.6%
c 575
 
5.6%
y 575
 
5.6%
x 575
 
5.6%
F 575
 
5.6%
6 575
 
5.6%
Other values (7) 4025
38.9%

artist_name
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
Taylor Swift
575 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters6900
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTaylor Swift
2nd rowTaylor Swift
3rd rowTaylor Swift
4th rowTaylor Swift
5th rowTaylor Swift

Common Values

ValueCountFrequency (%)
Taylor Swift 575
100.0%

Length

2024-01-07T20:36:52.953089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-07T20:36:53.165998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
taylor 575
50.0%
swift 575
50.0%

Most occurring characters

ValueCountFrequency (%)
T 575
8.3%
a 575
8.3%
y 575
8.3%
l 575
8.3%
o 575
8.3%
r 575
8.3%
575
8.3%
S 575
8.3%
w 575
8.3%
i 575
8.3%
Other values (2) 1150
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5175
75.0%
Uppercase Letter 1150
 
16.7%
Space Separator 575
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 575
11.1%
y 575
11.1%
l 575
11.1%
o 575
11.1%
r 575
11.1%
w 575
11.1%
i 575
11.1%
f 575
11.1%
t 575
11.1%
Uppercase Letter
ValueCountFrequency (%)
T 575
50.0%
S 575
50.0%
Space Separator
ValueCountFrequency (%)
575
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6325
91.7%
Common 575
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 575
9.1%
a 575
9.1%
y 575
9.1%
l 575
9.1%
o 575
9.1%
r 575
9.1%
S 575
9.1%
w 575
9.1%
i 575
9.1%
f 575
9.1%
Common
ValueCountFrequency (%)
575
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 575
8.3%
a 575
8.3%
y 575
8.3%
l 575
8.3%
o 575
8.3%
r 575
8.3%
575
8.3%
S 575
8.3%
w 575
8.3%
i 575
8.3%
Other values (2) 1150
16.7%

artist_popularity
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
120
575 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1725
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row120
2nd row120
3rd row120
4th row120
5th row120

Common Values

ValueCountFrequency (%)
120 575
100.0%

Length

2024-01-07T20:36:53.388887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-07T20:36:53.593716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
120 575
100.0%

Most occurring characters

ValueCountFrequency (%)
1 575
33.3%
2 575
33.3%
0 575
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1725
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 575
33.3%
2 575
33.3%
0 575
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1725
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 575
33.3%
2 575
33.3%
0 575
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1725
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 575
33.3%
2 575
33.3%
0 575
33.3%

album_id
Categorical

Distinct26
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
1NAmidJlEaVgA3MpcPFYGq
72 
1MPAXuTVL2Ej5x0JHiSPq8
46 
0PZ7lAru5FDFHuirTkWe9Z
 
34
6kZ42qRrzov54LcAk4onW9
 
30
4hDok0OAJd57SGIT8xuWJH
 
26
Other values (21)
367 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters12650
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1o59UpKw81iHR0HPiSkJR0
2nd row1o59UpKw81iHR0HPiSkJR0
3rd row1o59UpKw81iHR0HPiSkJR0
4th row1o59UpKw81iHR0HPiSkJR0
5th row1o59UpKw81iHR0HPiSkJR0

Common Values

ValueCountFrequency (%)
1NAmidJlEaVgA3MpcPFYGq 72
 
12.5%
1MPAXuTVL2Ej5x0JHiSPq8 46
 
8.0%
0PZ7lAru5FDFHuirTkWe9Z 34
 
5.9%
6kZ42qRrzov54LcAk4onW9 30
 
5.2%
4hDok0OAJd57SGIT8xuWJH 26
 
4.5%
1fnJ7k0bllNfL1kVdNVW1A 24
 
4.2%
6S6JQWzUrJVcJLK4fi74Fw 22
 
3.8%
1KVKqWeRuXsJDLTW0VuD29 22
 
3.8%
1o59UpKw81iHR0HPiSkJR0 22
 
3.8%
5AEDGbliTTfjOB8TSm1sxt 22
 
3.8%
Other values (16) 255
44.3%

Length

2024-01-07T20:36:53.824566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1namidjleavga3mpcpfygq 72
 
12.5%
1mpaxutvl2ej5x0jhispq8 46
 
8.0%
0pz7laru5fdfhuirtkwe9z 34
 
5.9%
6kz42qrrzov54lcak4onw9 30
 
5.2%
4hdok0oajd57sgit8xuwjh 26
 
4.5%
1fnj7k0bllnfl1kvdnvw1a 24
 
4.2%
6s6jqwzurjvcjlk4fi74fw 22
 
3.8%
1kvkqweruxsjdltw0vud29 22
 
3.8%
1o59upkw81ihr0hpiskjr0 22
 
3.8%
5aedgblittfjob8tsm1sxt 22
 
3.8%
Other values (16) 255
44.3%

Most occurring characters

ValueCountFrequency (%)
1 439
 
3.5%
J 439
 
3.5%
A 425
 
3.4%
V 352
 
2.8%
4 305
 
2.4%
P 301
 
2.4%
0 291
 
2.3%
c 288
 
2.3%
i 288
 
2.3%
T 279
 
2.2%
Other values (52) 9243
73.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5516
43.6%
Lowercase Letter 4632
36.6%
Decimal Number 2502
19.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J 439
 
8.0%
A 425
 
7.7%
V 352
 
6.4%
P 301
 
5.5%
T 279
 
5.1%
F 276
 
5.0%
W 275
 
5.0%
S 259
 
4.7%
Z 244
 
4.4%
G 244
 
4.4%
Other values (16) 2422
43.9%
Lowercase Letter
ValueCountFrequency (%)
c 288
 
6.2%
i 288
 
6.2%
u 263
 
5.7%
q 258
 
5.6%
g 245
 
5.3%
k 238
 
5.1%
f 214
 
4.6%
l 209
 
4.5%
o 191
 
4.1%
d 191
 
4.1%
Other values (16) 2247
48.5%
Decimal Number
ValueCountFrequency (%)
1 439
17.5%
4 305
12.2%
0 291
11.6%
2 269
10.8%
5 241
9.6%
6 223
8.9%
9 194
7.8%
7 189
7.6%
8 180
7.2%
3 171
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 10148
80.2%
Common 2502
 
19.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 439
 
4.3%
A 425
 
4.2%
V 352
 
3.5%
P 301
 
3.0%
c 288
 
2.8%
i 288
 
2.8%
T 279
 
2.7%
F 276
 
2.7%
W 275
 
2.7%
u 263
 
2.6%
Other values (42) 6962
68.6%
Common
ValueCountFrequency (%)
1 439
17.5%
4 305
12.2%
0 291
11.6%
2 269
10.8%
5 241
9.6%
6 223
8.9%
9 194
7.8%
7 189
7.6%
8 180
7.2%
3 171
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 439
 
3.5%
J 439
 
3.5%
A 425
 
3.4%
V 352
 
2.8%
4 305
 
2.4%
P 301
 
2.4%
0 291
 
2.3%
c 288
 
2.3%
i 288
 
2.3%
T 279
 
2.2%
Other values (52) 9243
73.1%

album_name
Categorical

MISSING 

Distinct24
Distinct (%)4.7%
Missing62
Missing (%)10.8%
Memory size4.6 KiB
Lover
72 
folklore: the long pond studio sessions (from the Disney+ special) [deluxe edition]
34 
Red (Taylor's Version)
 
30
Fearless (Taylor's Version)
 
26
Midnights (The Til Dawn Edition)
 
24
Other values (19)
327 

Length

Max length83
Median length26
Mean length23.173489
Min length4

Characters and Unicode

Total characters11888
Distinct characters49
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1989 (Taylor's Version) [Deluxe]
2nd row1989 (Taylor's Version) [Deluxe]
3rd row1989 (Taylor's Version) [Deluxe]
4th row1989 (Taylor's Version) [Deluxe]
5th row1989 (Taylor's Version) [Deluxe]

Common Values

ValueCountFrequency (%)
Lover 72
 
12.5%
folklore: the long pond studio sessions (from the Disney+ special) [deluxe edition] 34
 
5.9%
Red (Taylor's Version) 30
 
5.2%
Fearless (Taylor's Version) 26
 
4.5%
Midnights (The Til Dawn Edition) 24
 
4.2%
1989 (Taylor's Version) [Deluxe] 22
 
3.8%
Speak Now (Taylor's Version) 22
 
3.8%
Red (Deluxe Edition) 22
 
3.8%
Speak Now (Deluxe Package) 22
 
3.8%
1989 (Taylor's Version) 21
 
3.7%
Other values (14) 218
37.9%
(Missing) 62
 
10.8%

Length

2024-01-07T20:36:54.128863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
version 171
 
10.4%
deluxe 153
 
9.3%
taylor's 121
 
7.4%
edition 119
 
7.2%
the 92
 
5.6%
1989 75
 
4.6%
lover 72
 
4.4%
folklore 67
 
4.1%
fearless 61
 
3.7%
now 58
 
3.5%
Other values (25) 656
39.9%

Most occurring characters

ValueCountFrequency (%)
e 1258
 
10.6%
1132
 
9.5%
o 932
 
7.8%
i 765
 
6.4%
s 709
 
6.0%
r 661
 
5.6%
l 627
 
5.3%
n 606
 
5.1%
a 460
 
3.9%
t 384
 
3.2%
Other values (39) 4354
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8422
70.8%
Space Separator 1132
 
9.5%
Uppercase Letter 1019
 
8.6%
Close Punctuation 387
 
3.3%
Open Punctuation 387
 
3.3%
Decimal Number 352
 
3.0%
Other Punctuation 155
 
1.3%
Math Symbol 34
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1258
14.9%
o 932
11.1%
i 765
9.1%
s 709
8.4%
r 661
7.8%
l 627
 
7.4%
n 606
 
7.2%
a 460
 
5.5%
t 384
 
4.6%
d 372
 
4.4%
Other values (12) 1648
19.6%
Uppercase Letter
ValueCountFrequency (%)
T 184
18.1%
D 143
14.0%
V 137
13.4%
E 85
8.3%
S 81
7.9%
L 80
7.9%
F 69
 
6.8%
N 58
 
5.7%
M 57
 
5.6%
R 52
 
5.1%
Other values (3) 73
 
7.2%
Decimal Number
ValueCountFrequency (%)
9 150
42.6%
8 83
23.6%
1 75
21.3%
3 20
 
5.7%
0 16
 
4.5%
2 8
 
2.3%
Close Punctuation
ValueCountFrequency (%)
) 331
85.5%
] 56
 
14.5%
Open Punctuation
ValueCountFrequency (%)
( 331
85.5%
[ 56
 
14.5%
Other Punctuation
ValueCountFrequency (%)
' 121
78.1%
: 34
 
21.9%
Space Separator
ValueCountFrequency (%)
1132
100.0%
Math Symbol
ValueCountFrequency (%)
+ 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9441
79.4%
Common 2447
 
20.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1258
13.3%
o 932
 
9.9%
i 765
 
8.1%
s 709
 
7.5%
r 661
 
7.0%
l 627
 
6.6%
n 606
 
6.4%
a 460
 
4.9%
t 384
 
4.1%
d 372
 
3.9%
Other values (25) 2667
28.2%
Common
ValueCountFrequency (%)
1132
46.3%
) 331
 
13.5%
( 331
 
13.5%
9 150
 
6.1%
' 121
 
4.9%
8 83
 
3.4%
1 75
 
3.1%
] 56
 
2.3%
[ 56
 
2.3%
+ 34
 
1.4%
Other values (4) 78
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1258
 
10.6%
1132
 
9.5%
o 932
 
7.8%
i 765
 
6.4%
s 709
 
6.0%
r 661
 
5.6%
l 627
 
5.3%
n 606
 
5.1%
a 460
 
3.9%
t 384
 
3.2%
Other values (39) 4354
36.6%
Distinct23
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2019-08-23
72 
2017-11-09
46 
2008-11-11
 
35
2020-11-25
 
34
2014-01-01
 
32
Other values (18)
356 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5750
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-10-27
2nd row2023-10-27
3rd row2023-10-27
4th row2023-10-27
5th row2023-10-27

Common Values

ValueCountFrequency (%)
2019-08-23 72
 
12.5%
2017-11-09 46
 
8.0%
2008-11-11 35
 
6.1%
2020-11-25 34
 
5.9%
2014-01-01 32
 
5.6%
2021-11-12 30
 
5.2%
2010-10-25 30
 
5.2%
2021-04-09 26
 
4.5%
2027-05-26 24
 
4.2%
2012-10-22 22
 
3.8%
Other values (13) 224
39.0%

Length

2024-01-07T20:36:54.412676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-08-23 72
 
12.5%
2017-11-09 46
 
8.0%
2008-11-11 35
 
6.1%
2020-11-25 34
 
5.9%
2014-01-01 32
 
5.6%
2021-11-12 30
 
5.2%
2010-10-25 30
 
5.2%
2021-04-09 26
 
4.5%
2027-05-26 24
 
4.2%
2023-10-27 22
 
3.8%
Other values (13) 224
39.0%

Most occurring characters

ValueCountFrequency (%)
0 1317
22.9%
2 1276
22.2%
- 1150
20.0%
1 1109
19.3%
7 185
 
3.2%
9 174
 
3.0%
8 172
 
3.0%
3 137
 
2.4%
4 89
 
1.5%
5 88
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4600
80.0%
Dash Punctuation 1150
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1317
28.6%
2 1276
27.7%
1 1109
24.1%
7 185
 
4.0%
9 174
 
3.8%
8 172
 
3.7%
3 137
 
3.0%
4 89
 
1.9%
5 88
 
1.9%
6 53
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 1150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1317
22.9%
2 1276
22.2%
- 1150
20.0%
1 1109
19.3%
7 185
 
3.2%
9 174
 
3.0%
8 172
 
3.0%
3 137
 
2.4%
4 89
 
1.5%
5 88
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1317
22.9%
2 1276
22.2%
- 1150
20.0%
1 1109
19.3%
7 185
 
3.2%
9 174
 
3.0%
8 172
 
3.0%
3 137
 
2.4%
4 89
 
1.5%
5 88
 
1.5%
Distinct17
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
22
88 
18
72 
34
64 
16
48 
46
46 
Other values (12)
257 

Length

Max length8
Median length2
Mean length2.1426087
Min length1

Characters and Unicode

Total characters1232
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22
2nd row22
3rd row22
4th row22
5th row22

Common Values

ValueCountFrequency (%)
22 88
15.3%
18 72
12.5%
34 64
11.1%
16 48
8.3%
46 46
8.0%
19 38
 
6.6%
17 34
 
5.9%
26 26
 
4.5%
13 26
 
4.5%
24 24
 
4.2%
Other values (7) 109
19.0%

Length

2024-01-07T20:36:54.684435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
22 88
15.3%
18 72
12.5%
34 64
11.1%
16 48
8.3%
46 46
8.0%
19 38
 
6.6%
17 34
 
5.9%
13 26
 
4.5%
26 26
 
4.5%
24 24
 
4.2%
Other values (7) 109
19.0%

Most occurring characters

ValueCountFrequency (%)
1 284
23.1%
2 267
21.7%
4 148
12.0%
6 120
9.7%
3 90
 
7.3%
8 80
 
6.5%
9 38
 
3.1%
0 35
 
2.8%
7 34
 
2.8%
e 30
 
2.4%
Other values (7) 106
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1112
90.3%
Lowercase Letter 105
 
8.5%
Uppercase Letter 15
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 284
25.5%
2 267
24.0%
4 148
13.3%
6 120
10.8%
3 90
 
8.1%
8 80
 
7.2%
9 38
 
3.4%
0 35
 
3.1%
7 34
 
3.1%
5 16
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
e 30
28.6%
h 15
14.3%
i 15
14.3%
r 15
14.3%
t 15
14.3%
n 15
14.3%
Uppercase Letter
ValueCountFrequency (%)
T 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1112
90.3%
Latin 120
 
9.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 284
25.5%
2 267
24.0%
4 148
13.3%
6 120
10.8%
3 90
 
8.1%
8 80
 
7.2%
9 38
 
3.4%
0 35
 
3.1%
7 34
 
3.1%
5 16
 
1.4%
Latin
ValueCountFrequency (%)
e 30
25.0%
T 15
12.5%
h 15
12.5%
i 15
12.5%
r 15
12.5%
t 15
12.5%
n 15
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 284
23.1%
2 267
21.7%
4 148
12.0%
6 120
9.7%
3 90
 
7.3%
8 80
 
6.5%
9 38
 
3.1%
0 35
 
2.8%
7 34
 
2.8%
e 30
 
2.4%
Other values (7) 106
 
8.6%

Interactions

2024-01-07T20:36:33.014066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:03.378306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:05.971360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:08.545034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:11.197549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:13.964445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:16.676694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:19.306065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:22.167600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:24.815315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:27.578813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:30.360666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:33.228888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:03.584676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:06.173033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:08.748772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:11.415511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:14.164690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:16.898939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:19.527453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:22.388404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:25.027446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:27.784297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:30.577004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:33.443380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:03.787275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:06.378124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:08.956945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:11.641201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:14.515366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:17.101998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:19.781287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:22.591801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:25.335358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:28.166002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:30.795421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:33.670230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:04.001517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:06.590050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:09.179688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:11.866958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:14.728922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:17.318180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:20.016481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:22.806629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:25.563197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:28.388862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:31.010913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:33.916457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:04.236491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:06.812317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:09.411220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:12.103810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:14.967178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:17.554247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:20.284084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:23.047181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:25.794214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:28.624491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:31.244056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:34.133073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:04.445122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:07.018009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:09.626740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:12.316055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:15.152421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:17.755622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:20.501655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:23.238235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:25.998540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:28.821377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:31.443707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:34.356266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:04.649348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:07.220728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:09.840543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:12.537558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:15.362416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:17.983011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:20.737302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:23.462827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:26.220206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:29.042202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:31.662703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:34.596396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:04.896873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:07.473612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:10.091561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:12.802022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:15.606051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:18.220434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:21.003015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:23.710826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:26.479918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:29.283674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:31.919718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:34.841551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:05.104130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:07.681307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:10.303637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:13.035844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:15.817664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:18.432499image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:21.235573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:23.921530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:26.696117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:29.497698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:32.133326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:35.068614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:05.324879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:07.888514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:10.526211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:13.274001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:16.041055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:18.657192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:21.469567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:24.153434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:26.913517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:29.722596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:32.357921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:35.286411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:05.531885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:08.105185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:10.742625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:13.499362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:16.242666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:18.866986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:21.697273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:24.377546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:27.124247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:29.925522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:32.574632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:35.498531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:05.731407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:08.310466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:10.956677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:13.725276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:16.451163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:19.083214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:21.929045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:24.581906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:27.343818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:30.127498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-07T20:36:32.777410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-07T20:36:35.910391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-07T20:36:36.877310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

disc_numberduration_msexplicittrack_numbertrack_popularitytrack_idtrack_nameaudio_features.danceabilityaudio_features.energyaudio_features.keyaudio_features.loudnessaudio_features.modeaudio_features.speechinessaudio_features.acousticnessaudio_features.instrumentalnessaudio_features.livenessaudio_features.valenceaudio_features.tempoaudio_features.idaudio_features.time_signatureartist_idartist_nameartist_popularityalbum_idalbum_namealbum_release_datealbum_total_tracks
01212600False1774WUepByoeqcedHoYhSNHRtWelcome To New York (Taylor's Version)0.7570.6107.0-4.84010.03270.0094203.66e-050.36700.685116.9984WUepByoeqcedHoYhSNHRt4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
11231833False2780108kcWLnn2HlH2kedi1gnBlank Space (Taylor's Version)0.7330.7330.0-5.37610.06705.00000000.16800.70196.0570108kcWLnn2HlH2kedi1gn4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
21231000False3793Vpk1hfMAQme8VJ0SNRSkdStyle (Taylor's Version)0.5110.82211.0-4.78500.03970.0004210.01970.08990.30594.8683Vpk1hfMAQme8VJ0SNRSkd4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
31235800False4781OcSfkeCg9hRC2sFKB4IMJOut Of The Woods (Taylor's Version)0.5450.8850.0-5.96810.0447-0.0005375.59e-050.38500.20692.0211OcSfkeCg9hRC2sFKB4IMJ4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
41193289False5772k0ZEeAqzvYMcx9Qt5aClQAll You Had To Do Was Stay (Taylor's Version)0.5880.7210.0-5.57910.03170.00065600.13100.52096.9972k0ZEeAqzvYMcx9Qt5aClQ4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
51219209False67650yNTF0Od55qnHLxYsA5PwShake It Off (Taylor's Version)0.6360.8087.0-5.69310.07290.0121002.18e-050.35900.917160.05850yNTF0Od55qnHLxYsA5Pw4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
61207650False7763FxJDucHWdw6caWTKO5b23I Wish You Would (Taylor's Version)0.6700.8580.0-6.52810.0439-0.0035401.25e-050.06870.539118.0093FxJDucHWdw6caWTKO5b234.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
71211103False8757oZONwFiFIErZcXAtTu7FYBad Blood (Taylor's Version)0.6180.6837.0-6.43810.19400.03620000.30500.363169.9717oZONwFiFIErZcXAtTu7FY4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
81220433False97627exgla7YBw9DUNNcTIpjyWildest Dreams (Taylor's Version)0.5890.6748.0-7.48010.06560.0436007.18e-050.11200.514139.98527exgla7YBw9DUNNcTIpjy4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
91247533False1076733OhaXQIHY7BKtY3vnSknHow You Get The Girl (Taylor's Version)0.7580.6915.0-5.79810.05150.0019601.09e-050.09390.538119.997733OhaXQIHY7BKtY3vnSkn4.006HL4z0CvFAxyc27GXTaylor Swift1201o59UpKw81iHR0HPiSkJR01989 (Taylor's Version) [Deluxe]2023-10-2722
disc_numberduration_msexplicittrack_numbertrack_popularitytrack_idtrack_nameaudio_features.danceabilityaudio_features.energyaudio_features.keyaudio_features.loudnessaudio_features.modeaudio_features.speechinessaudio_features.acousticnessaudio_features.instrumentalnessaudio_features.livenessaudio_features.valenceaudio_features.tempoaudio_features.idaudio_features.time_signatureartist_idartist_nameartist_popularityalbum_idalbum_namealbum_release_datealbum_total_tracks
5651207106False6595Tj2MqcFMf60CaGsKbM1aqThe Outside0.5890.8055.0-4.05510.02930.0049100.24000.591112.9825Tj2MqcFMf60CaGsKbM1aq4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5661248106False7582zzxwmoOBnXDT0KnJsoIWkTied Together with a Smile0.4790.5782.0-4.96310.02940.5250000.08410.192146.1652zzxwmoOBnXDT0KnJsoIWk4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5671236053False85841sjzdjScVwnxnxADElts6Stay Beautiful0.5940.6298.0-4.91910.02460.0868000.13700.504131.59741sjzdjScVwnxnxADElts64.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5681242200False9656CdaXOq1MWe2JHDalTG01dShould've Said No0.4760.7774.0-3.77100.02890.0103000.19600.472167.9646CdaXOq1MWe2JHDalTG01d4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5691213080False10602O8sogKJCfVZ4rotBv1vVFMary's Song (Oh My My My)0.4030.6272.0-5.28010.02920.0177000.18200.37474.9002O8sogKJCfVZ4rotBv1vVF4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5701201106False11701j6gmK6u4WNI33lMZ8dC1sOur Song0.6680.6722.0-4.93110.03030.1110000.32900.53989.0111j6gmK6u4WNI33lMZ8dC1s4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5711213053False12607CzxXgQXurKZCyHz9ufbo1I'm Only Me When I'm With You0.5630.9348.0-3.62910.06462.000000.0008070.10300.518143.9647CzxXgQXurKZCyHz9ufbo14.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5721203226False13581k3PzDNjg38cWqOvL4M9vqInvisible0.6120.3947.0-5.72310.02430.6370000.14700.23396.0011k3PzDNjg38cWqOvL4M9vq4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5731220146False14580YgHuReCSPwTXYny7isLjaA Perfectly Good Heart0.4830.7514.0-5.72610.03650.0034900.12800.268156.0920YgHuReCSPwTXYny7isLja4.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen
5741179066False15581hxLyjC9D9Jpw6EAPKqWv4Teardrops on My Guitar - Pop Version0.4590.75310.0-3.82710.05370.0402000.08630.483199.9971hxLyjC9D9Jpw6EAPKqWv44.006HL4z0CvFAxyc27GXTaylor Swift1205eyZZoQEFQWRHkV2xgAeBwTaylor Swift1989-10-24Thirteen

Duplicate rows

Most frequently occurring

disc_numberduration_msexplicittrack_numbertrack_popularitytrack_idtrack_nameaudio_features.danceabilityaudio_features.energyaudio_features.keyaudio_features.loudnessaudio_features.modeaudio_features.speechinessaudio_features.acousticnessaudio_features.instrumentalnessaudio_features.livenessaudio_features.valenceaudio_features.tempoaudio_features.idaudio_features.time_signatureartist_idartist_nameartist_popularityalbum_idalbum_namealbum_release_datealbum_total_tracks# duplicates
01150440False17721SmiQ65iSAbPto6gPFlBYmIt’s Nice To Have A Friend0.7370.17510.0-9.91210.04010.971000.0003370.17100.54570.0081SmiQ65iSAbPto6gPFlBYm4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23184
11170640False17743rA71bccXFGD4C8GOpIlNI Forgot That You Existed0.6640.3165.0-10.34510.51900.298002.03e-060.08120.54192.87543rA71bccXFGD4C8GOpIlN4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23184
21171360False14846RRNNciQGZEXnqk8SQ9yv5You Need To Calm Down0.7710.6712.0-5.61710.05530.0092900.06370.71485.0266RRNNciQGZEXnqk8SQ9yv54.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23184
31173386False6782YWtcWi3a83pdEg3Gif4PdI Think He Knows0.8970.3660.0-8.02910.05690.008890.0003530.07150.416100.0032YWtcWi3a83pdEg3Gif4Pd4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23184
61190240False11801LLXZFeAHK9R4xUramtUKwLondon Boy0.6950.7101.0-6.63910.05000.024600.0001040.13300.557157.9251LLXZFeAHK9R4xUramtUKw4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23184
71190360False4863RauEVgRgj1IuWdJ9fDs70The Man0.7770.6580.0-5.19110.05400.0767000.09010.633110.0483RauEVgRgj1IuWdJ9fDs704.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23184
81193000False16802Rk4JlNc2TPmZe2af99d45ME! (feat. Brendon Urie of Panic! At The Disco)0.6100.8300.0-4.10510.05710.0330000.11800.728182.1622Rk4JlNc2TPmZe2af99d454.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23184
91198533False10792dgFqt3w9xIQRjhPtwNk3DDeath By A Thousand Cuts0.7120.7324.0-6.75410.06290.4540000.31900.31394.0712dgFqt3w9xIQRjhPtwNk3D4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23184
101200306False13785hQSXkFgbxjZo9uCwd11soFalse God0.7390.32011.0-10.86200.23900.736000.0001470.11100.35179.9705hQSXkFgbxjZo9uCwd11so4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23184
111201586False12724AYtqFyFbX0Xkc2wtcygTrSoon You’ll Get Better (feat. The Chicks)0.4330.1820.0-12.56610.06410.9070000.12300.421207.4764AYtqFyFbX0Xkc2wtcygTr4.006HL4z0CvFAxyc27GXTaylor Swift1201NAmidJlEaVgA3MpcPFYGqLover2019-08-23184